Triple
T30864587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belfort Gap |
E786161
|
entity |
| Predicate | lowestPointBetween |
P211
|
FINISHED |
| Object | Vosges Mountains |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Vosges Mountains | Statement: [Belfort Gap, lowestPointBetween, Vosges Mountains]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lowestPointBetween Context triple: [Belfort Gap, lowestPointBetween, Vosges Mountains]
-
A.
lowestPoint
chosen
Indicates that one entity is the point with the minimum vertical position or value relative to another entity or within a specified context.
-
B.
lowestPointsNear
Indicates that one entity identifies or specifies the locations of the lowest points in elevation or value in the vicinity of another entity.
-
C.
bottomPointRepresents
Indicates that a specific bottom point in a representation corresponds to or stands for another entity, value, or feature in the modeled system.
-
D.
lowestZone
Indicates that one entity occupies or corresponds to the lowest-ranked or lowest-level zone among a set of zones.
-
E.
lowestLatitude
Indicates that one entity has a latitude value that is lower (i.e., farther south) than another entity or than all others in a given set.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f224b9df2c819086f55f8bcf7f382e |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f691ab31288190afe04c1a55477a9f |
completed | May 3, 2026, 12:07 a.m. |
| PD | Predicate disambiguation | batch_69f68b7d2794819092fef8a63f4f3de8 |
completed | May 2, 2026, 11:40 p.m. |
Created at: April 29, 2026, 8:47 p.m.